behavioral-reasons-for-new-product-failure

Behavioral Reasons for New Product Failure: Does Overconfidence Induce Over-forecasts? Dmitri G Markovitch, Joel H Steckel, Anne Michaut-Denizeau, Deepu Philip, and William M Tracy ∗ Journal of Product Innovation Management, Second Submission June 13, 2014 ∗ Dmitri G Markovitch is Assistant Professor of Marketing at Lally School of Management, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, Ph 518-276-2197, Fax 518-276-8661, markod@rpi.edu Joel H Steckel is Professor of Marketing at Leonard N Stern School of Business, New York University, Tisch Hall, 40 West 4th Street, Room 812, New York, NY 10012, Ph 212-998-0521, Fax 212-995-4006, jsteckel@stern.nyu.edu Anne Michaut-Denizeau is Affiliate Professor of Marketing at HEC-Paris, 1, rue de la Libération, 78351 Jouy en Josas cedex, France, Ph 33-1-39-67-94-26, Fax 33-1-39-67-70-87, michaut@hec.fr Deepu Philip is Assistant Professor of Computer Science at Indian Institute of Technology, Kanpur, Kanpur 208016, UP, India, Ph 91-512-2597460, dphilip@iitk.ac.in William M Tracy is Assistant Professor of Strategy at Lally School of Management, Rensselaer Polytechnic Institute 110 8th Street, Troy, NY 12180, Ph 518-2762225, Fax 518-276-8661, tracyw@rpi.edu The authors thank Peter Golder and Gina O’Connor for their helpful comments Behavioral Reasons for New Product Failure: Does Overconfidence Induce Over-forecasts? Abstract We empirically investigate one specific cognitive distortion heretofore neglected in studies of new product commercialization—overconfidence, commonly defined in the literature as excessive belief in own abilities to generate superior performance To lay the groundwork for our study, we develop a behavioral model which both organizes wellunderstood new product performance determinants and illuminates others heretofore not studied, namely, incentive alignment and cognitive limitations and biases The model summarizes extant research and allows us to develop research hypotheses related to overconfidence We find that decision makers’ overconfidence is associated with a higher likelihood of over-forecasting new product sales The observed effect is fully mediated by tactical decisions that dampen demand, namely elevated product pricing We conclude with a discussion of our results and provide specific recommendations for practice Keywords: overconfidence, new product development, innovation, new product performance, failure, managerial decision making, cognitive biases Introduction Reducing high new product failure rates remains one of the greatest challenges of new product research (e.g., Barczak, Griffin, and Kahn, 2009; Wind and Mahajan, 1997) In response, a number of scholars have identified and categorized various determinants of new product success or failure (e.g., Cooper and Kleinschmidt, 1990; Henard and Szymanski, 2001; Montoya-Weiss and Calantone, 1994) Although those studies have greatly expanded our understanding of what drives new product performance, they tend to explore a relatively constant subset of drivers In particular, these frameworks have not considered classes of factors that pertain to the decision unit’s incentive structures and cognitive limitations Studies in marketing, economics, finance, and management consistently demonstrate that managers’ incentives and characteristics, including cognitive limitations, affect firm decisions and performance (e.g., Currim, Lim, and Kim, 2012; Graham, Harvey, and Puri, 2013; Hirshleifer, Low, and Teoh, 2012) In this paper, we investigate one specific cognitive bias heretofore neglected in studies of new product commercialization—overconfidence Overconfidence is commonly defined in the literature as excessive belief in own abilities to generate superior performance (Clark and Friesen, 2009; Hirshleifer, Low, and Teoh, 2012; Malmendier and Tate, 2005; Moore and Healy, 2007) Assessment of confidence and its impact on human decision making has been a prominent area of research in cognitive psychology over the past half-century (Benabou and Tirole, 2002; Moore and Healy, 2007) In the past decade, its importance has filtered into business disciplines, as evidenced by a veritable explosion of research on overconfidence in the management and finance literatures The newly formed “Judgment and Decision Making” department in the journal Management Science highlights a need for more business research on “assessments of confidence” in its current editorial statement (Management Science, 2014) Simply put, the heightened emphasis on overconfidence in business research is motivated by greater appreciation of its impact in decision making Researchers associate overconfidence, in particular, with serious judgment errors in various domains of human activity, including corporate investments (Malmedier and Tate, 2005, Roll, 1986, Malmedier and Tate, 2008, Gervais, Heaton, and Odean, 2011, Odean, 1999) Summarizing the relevant evidence, Plous (1993, p 217) states: “No problem in judgment and decision making is more prevalent and more potentially catastrophic than overconfidence.” We address two research questions about the impact of this bias on new product commercialization activities First, we explore whether overconfidence is associated with over-forecasting new product demand Second, we investigate two complementary mechanisms that may account for overconfidence-induced over-forecasts Our findings are based on data generated in the course of management simulation workshops conducted among graduate students at three leading business schools in India To lay the groundwork for our study, we develop a model which both organizes wellunderstood new product performance determinants and illuminates others heretofore not studied, namely, incentive alignment and cognitive limitations and biases We summarize extant research in this behavioral model intended to facilitate general hypothesis development We then use the model to develop research hypotheses related to the portion of the model that addresses overconfidence In the next section, we present our model that summarizes nine established and two newly-proposed categories of new product performance determinants by linking them to key behaviors in the new product development (NPD) process This model contextualizes the hypothesized impact of overconfidence and other cognitive limitations We then state our research hypotheses followed by the empirical investigation We conclude with a discussion of our results and their implications for research and practice Generalized Model of New Product Failure Multiple factors contribute to a new product’s performance in the marketplace Extant literature groups those factors in a large number of similar categories (e.g., Cooper and Kleinshmidt, 1990; de Brentani, 1991; Di Benedetto, 1999; Henard and Szymanski, 2001) We summarize this literature in the form of a generalized framework (shown schematically in Figure and further detailed in Table 1) that incorporates both previously identified and our newly proposed determinants of new product failure (the latter are flawed incentive structures and decision unit limitations) in a multi-level structure We frame new product outcomes in terms of failure rather than success to provide for a more pointed discussion Superior performance on at least one key antecedent is a necessary, but not sufficient condition for success Adequate performance on most antecedents is also required for new product success In contrast, failure on a single antecedent can often prove decisive For the purposes of the current research, we define failure broadly as the inability to meet previously set objectives (e.g., Cooper, 1979; Maidique and Zirger, 1985) > We hierarchically arrange new product performance antecedents according to their longitudinal sequence, whereby some conditions and activities precede and influence or serve as inputs for subsequent activities The spine of the model reflects the behavioral sequence of steps in the NPD process: analysis and interpretation, decision response, execution Although NPD is commonly treated as a multi-stage process, the aggregate three-step representation captures the distinct behavioral dimensions of NPD activities in the following fashion Managers look to their business environment for new product ideas Information about market needs, trends, and competitive offerings serves as input for decisions to modify existing products or develop new ones The environmental analysis and interpretation serves as the basis for a managerial decision response with respect to project selection, continuation and launch In the latter step, the firm also specifies a new product offering together with a business model through which the offering is to be commercialized The firm then executes these decisions in the development process and commercialization Because NPD and eventual launch are learning processes, firms routinely consider both internal and external feedback and update analysis, decisions, and execution as these (and subsequent) steps unfold As such, most determinants of failure flow through the three “spinal” activities in Figure “Foundational” determinants, listed above the spine, are those inputs and structural elements that support (or inhibit) the spinal activities (e.g., faulty market research or resource limitations) They provide the foundation for the underlying NPD behavioral process, i.e., analysis and interpretation, decision response, and execution “Byproduct” determinants, listed below the spine, are the byproducts of inadequate analysis and interpretation, decision response, or execution that form more proximate causes of marketplace failure (i.e., those marketing and operations missteps that prevent the product from thriving) It is worth noting that the execution step represents a very broad behavioral category We keep it in the aggregated form for the sake of parsimony Also, the managerial steps, or sets of activities, in the spine of the model map closely, but not one-to-one, to the three components of the market orientation concept (e.g., Kohli and Jaworski, 1990): (1) activities to gather information on customer wants and needs; (2) the use of cross-functional teams to analyze the information; and (3) value creation We group the foundational and byproduct determinants into the five categories above and below the spine, respectively, shown in Figure The locus of their proposed impact is indicated by the dashed arrows.1 To keep the model tractable, we not postulate relationships among determinants at the same level of the hierarchy in Figure Some The proposed arrows reflect what we view as primary flows While other linkages are possible, they are likely to be of more indirect nature determinants may be linked by moderating or mediating relationships (as we demonstrate empirically with respect to pricing and over-forecasts) We accommodate this extra complexity by placing byproduct determinants within a general flow (represented in the wide arrow) that leads towards new product failure The proposed categories are sufficiently general to capture most known and newly-proposed antecedents For example, most issues pertaining to a new product (e.g., mis-specification, no reason to be, or flawed design) will fall in one of our two product-related categories: “Weak Value Proposition” or “Low Product Quality.” The model reflects the idea that weaknesses in resources or structure impact analysis, decision, and/or execution Flaws in analysis, decision, and/or execution in turn produce marketing and/or operational missteps that lead to a higher likelihood of new product failure For example, managers’ cognitive limitations, such as overconfidence, may induce a systematic bias in the “analysis and interpretation” step that produces excessive expectations for new product performance (i.e., over-forecasts) and overproduction This view casts the foundational determinants as the root cause of new product failure The seeds of failure are planted there They grow through the behavioral components of the spine and emerge as the weeds that are the byproduct determinants of new product failure Stated differently, the model postulates that the key to preempting most marketing or operational missteps is in ensuring that the foundational determinants are properly addressed Additional failure determinants that are outside a firm’s control include a group of environmental factors, such as adverse competitive and market forces, that affect a new product’s performance after commercialization This group of factors generally occurs late in the temporal sequence of NPD activities and may moderate the impact of the other byproduct determinants on marketplace outcomes (Calantone, Schmidt, and Di Benedetto, 1997) As such, we place these factors in proximity to the outcome in Figure We note that other adverse forces can also directly impact byproduct determinants For example, the effectiveness of distribution efforts and product quality or reliability may be impaired by unanticipated component shortages or perturbations in the supply chain, such as disputes or strikes > Most of the antecedents implicit in Figure have been discussed in prior literature, either through conceptual frameworks, hypotheses advanced, or empirical study We summarize that research in Table (that is organized around the categories postulated in Figure 1) In Table 1, we pay particular attention to those antecedents that have been confirmed through meta-analyses or replication in multiple studies In addition to reframing and summarizing the impact of new product failure determinants in a longitudinal behavioral form, we argue that models and research into new product performance determinants should consider two important classes of factors—a decision unit’s limitations and incentive incompatibility between firm owners and managers as well as between layers of managers We summarize cross-disciplinary research that points to one of our new failure determinants, incentive incompatibility in Table However, owing to its focus in our empirical work, we provide a more developed rationale for considering decision unit limitations as foundational new product performance antecedents in the next section We substantiate our arguments by developing and testing specific hypotheses about how managerial overconfidence may produce flawed (byproduct) decisions that would hinder a new product’s performance after launch Managerial Overconfidence and Errors in the NPD Process Like all humans, managers suffer from limited information processing capacity (e.g., Kahneman, 2003; Simon, 1957) To cope, managers routinely resort to intuition- and heuristics-based decision-making processes (e.g., Bazerman and Moore, 2012; Kahneman and Tversky, 1979) In day-to-day activities, judgmental heuristics generally produce satisfactory outcomes (e.g., Gigerenzer and Selten, 2001) Unfortunately, heuristics also make decision makers susceptible to a variety of cognitive biases that often degrade decision quality in more complex situations The literature documents dozens of such biases (Bazerman and Moore, 2012; Sutherland, 2007) In particular, research has implicated overconfidence bias as an important factor in flawed decisions in contexts directly relevant to NPD, such as risk taking, resource allocation, and forecasting Overconfidence arises as a side effect of cognitive processes engaged in the maintenance and enhancement of self-esteem and self-confidence that are key factors in human motivation to act (Anderson et al., 2012; also, see Benabou and Tirole, 2002 for an overview) Empirical research shows that most individuals, including experts, are overconfident in general, but there is considerable variation among individuals (Biais et al., 2005; Kahneman and Tversky, 1992; Odean, 1999) Overconfidence also varies over time and across tasks (Benabou and Tirole, 2002) Overconfidence reflects a systematic miscalibration of one’s judgment and beliefs that results in more positive assessments of self and situation than is justified by the facts Overconfident managers tend to view challenges in an optimistic light (Lovallo and Kahneman, 2003), in part, because they overestimate the amount of control they have over outcomes (Moore and Healy, 2007; Presson and Benassi, 1996) and because they ignore risks (March and Shapira 1987) Voluminous research shows that individuals display a greater degree of overconfidence when faced with higher problem complexity (Alba and Hutchinson, 2000; Griffin and Tversky, 1992; Moore and Healy, 2007), suggesting that NPD (O’Connor, 2008), may present fertile ground for decisions tinged with this bias In the only published research on overconfidence in the NPD domain known to us, Simon and Houghton (2003) report a field study showing that overconfidence is associated with a higher likelihood of launching more pioneering (i.e., riskier) high-technology products that are less successful on average than more incremental innovations.2 Overconfidence manifests itself in overestimation of the accuracy and depth of one’s own knowledge (Alba and Hutchinson, 2000; Bazerman and Moore, 2012; Benabou and Tirole, 2002) This may arise from individuals’ tendency to underweight or ignore those aspects of a problem with which the decision maker is less familiar (Brenner, Koehler, and Tversky, 1996) As a result, overconfident individuals tend to over-rely on their basic knowledge and experience, and be relatively less engaged in evaluating new (or disconfirming) information that would allow them to further reduce uncertainty in a situation (Russo and Shoemaker, 1992) Such over-reliance on one’s basic knowledge and experience can be particularly problematic in the NPD context, because NPD activities often require perspectives that are novel and different from one’s past experience (O’Connor, 2008) This research implies that overconfidence may lead to flawed inputs for important NPD decisions and activities through inaccurate forecasts Accurate forecasting is predicated on effective information acquisition and use (Kahn, 2006) It also requires effective updating of one’s prior beliefs as new information becomes available However, the literature shows that overconfidence may hinder one’s ability to process and incorporate new information (Russo and Shoemaker, 1992) Multiple studies confirm that overconfidence affects individuals’ predictions of events in which the individuals participate In particular, these predictions/forecasts tend to be positively biased (e.g., Alba and Hutchinson, 2000; Camerer and Lovallo, 1999; Pulford and Colman, 1996) In sum, this literature suggests that managers may issue positively-biased new product forecasts as a direct byproduct of their overconfidence Stated formally, Hirshleifer, Low, and Teoh (2012) find that greater CEO overconfidence is associated with higher R&D expenditure and patenting output Unfortunately, research sheds little light on how a firm’s patenting output relates to new product performance specifically, since firms patent their inventions for various strategic reasons • Focus on organizational risk tolerance Too low a target failure rate discourages innovation Too high a target failure rate may encourage speculative projects   Managerial incentive structures that tie rewards and consequences to project characteristics and risk may prove effective at discouraging undesirable behaviors Limitations and Further Research Our paper’s limitations present opportunities for related research Empirical research on decision unit limitations, such as ours, concerns basic unobservable human decision processes and behaviors that are difficult to study in-vivo or ex-post (e.g., using methods that rely on key informants’ recollection of past events) To achieve internal validity, our approach and sample sacrifice a measure of external validity Despite the importance of context in NPD activity, basic human decision making often persists across contexts Without intervention, behavior in the laboratory can approximate behavior in the organization In fact, one of the objectives of laboratory research is to find interventions that will prevent certain basic behaviors from manifesting itself in applied contexts Likewise, we hope that our findings will give impetus to additional laboratory research and field studies that can corroborate our results, further expand our understanding of the role of managerial overconfidence in producing undesirable NPD outcomes, and suggest effective debiasing strategies This research can be extended even further to enhance external validity once the intervention is actually implemented by analyzing decision quality pre- and post- implementation Such a path demonstrates the complementary nature of invivo and laboratory research approaches An additional important limitation of our research arises from our data constraints It would be instructive to explore the impact of overconfidence on a full range of marketing decisions, including resource allocations to new product commercialization, and the innovativeness of new product configurations launched by overconfident managers It would 23 also be very valuable to explore other possible mechanisms that may mediate or moderate the impact of overconfidence in 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Irrationality Pinter and Martin Ltd., UK Troy, L C., Hirunyawipada, T., and Paswan, A K 2008 Cross-functional integration and new product success: An empirical investigation of the findings,” Journal of Marketing, 72(6): 132-146 Ulwick, A W 2002 Turn customer input into innovation Harvard Business Review, 80(1): 91-97 Urban, G L., and Hauser, R 1993 Design and Marketing of New Products Englewood Cliffs, NJ: Prentice Hall Whyte, G 1991 Decision failures: Why they occur and how to prevent them Academy of Management Executive, 5(3): 23-31 Wind, J., and Mahajan, V 1997 Issues and opportunities in new product development: An introduction to the special issue Journal of Marketing Research, 34(1): 1-12 Yoon, E., and Lilien, G L 1985 New industrial product performance: The effects of market characteristics and strategy Journal of Product Innovation Management, 2(3): 134144 Zajac, E J., and Bazerman, M H 1991 Blind spots in industry and competitor analysis: Implications of interfirm (mis):perceptions for strategic decisions Academy of Management Review, 16(1): 37-56 Zinkhan, G M., and Martin, J C R 1987 New brand names and inferential beliefs: Some insights on naming new products Journal of Business Research, 15(2): 157-172 Zirger, B J., and Maidique, M A 1990 A model of new product development: An empirical test Management Science, 36(7): 867-883 29 Table Known and Proposed Antecedents of New Product Failure1 Antecedent Description and Examples Faulty market research - Flawed market Inappropriate research methods or sampling may research planning or bias inferences or sales forecasts execution Resource limitations - Financial constraints Literature sources Henard & Szymanski 2001; Marquis 1969; Ottum & Moore 1997; Urban & Hauser 1993 Insufficient funding may lead to harmful compromises in execution, such as skipping or curtailing important steps, e.g., testing Page 1993 - Low senior management support Low senior management support may deprive project teams of critical inputs, such as help formulating new product strategies or developing a clear vision of objectives; funding, resources and cover so that work can continue unobstructed; and help incubating new-to-theworld technologies Brown & Eisenhardt 1995; Henard & Szymanski 2001; Imai, Nonaka, & Takeuchi 1985; O’Connor 2008 - Incompatible engineering skills Incompatible engineering skills and know-how can hamper a firm’s effort to specify a new product correctly and to develop it defect-free, on time and within budget Cooper & Kleinschmidt 1990; Henard & Szymanski 2001; Maidique & Zirger 1985 - Incompatible production process knowledge Lack of process knowledge increases the likelihood of manufacturing defects and hurts product quality Barnett & Clark 1996; Nevins & Whitney 1989 - Lack of marketing synergy A firm’s brand strength, supply chain and corporate reputation may provide minimal leverage in an unrelated product category, e.g., newcomers to a product category not have a supportive network of supplier, distributor and customer relationships that facilitate new product development, sourcing and commercialization Incentive Incompatibility The principal-agent literature postulates that organizations are made up of individuals who act in their own self-interest while pursuing organizational goals Calantone, Schmidt, & Song 1996; Di Benedetto 1999; Dutta, Narasimhan, & Rajiv 1999; Henard & Szymanski 2001; Hultink & Atuahene-Gima 2000 Jensen & Mekling 1976 Besides goal divergence between owners and managers, goals (and investment preferences) may differ among layers of management Harris, Kriebel, & Raviv 1982 Managers compete with each other in various domains A high number of NPD projects may allow a business unit to draw greater resources from the parent corporate entity that can be used not only for those projects but also to grow the business unit and increase its organizational influence Brass & Burkhardt 1993; Houston, Walker, Hutt, & Reingen 2001 Sales managers are willing to “bias their sales forecasts to suit their own interests as rational economic individuals.” Lowe and Shaw 1968 30 Structural Impediments - Low crossGreater participation or integration of key functional functions in the NPD process, including sales and participation or marketing has a positive impact on new product integration success Di Benedetto 1999; Ernst, Hoyer, & Rübsaamen 2010; Troy, Hirunyawipada, & Paswan 2008 Misforecasts New product sales forecasts are particularly influential, because they impact project selection, continuation and launch Misforecasts may lead to suboptimalities in a firm’s NPD pipeline and costly commercialization missteps Weak value proposition - Weak value To the extent that value proposition is a key proposition consideration in product purchase, a weak value proposition handicaps product sales Flawed marketing programs - Flawed product Weakness in salient product details, such as poor tactics appearance, brand name or packaging, have a negative impact on buyer behavior Ehrman & Shugan 1995; Kahn 2006 Cooper & Kleinschmidt 1990; Henard & Szymanski 2001; Maidique & Zirger 1984 Cooper, Gulen, & Rau 2005; Owen 1986; Zinkhan & Martin 1987 - Flawed pricing tactics Price-level decisions impact both product profitability and value to customers Mispricing a product in either direction, thus, can greatly diminish its financial performance Smith 2012 - Flawed distribution approaches, including poorly trained or incentivized sales force A flawed distribution approach or sales effort impairs a firm’s ability to reach target markets effectively or generate sales In contrast, a sound distribution strategy may also benefit new product performance through synergies with other elements of commercialization strategy, such as pricing Di Benedetto 1999; Hultink & Atuahene-Gima 2000 - Flawed promotion tactics Flawed promotion tactics impair a firm’s ability to reach target markets, communicate the value of a new product offering or stimulate purchasing Calantone, Schmidt, & Song 1996; Di Benedetto 1999; Song & Parry 1994 - Weak launch effort Because new products compete with incumbents for limited distribution space and share of wallet, they require considerable marketing support over time to penetrate a market Cooper 1979; Maidique & Zirger 1984 Lack of overall quality, or excellence on dimensions, such as appearance, performance, ease of use, workmanship, materials, reliability, durability and safety has a negative impact on market success over time Adverse market conditions - Small or stagnant Larger markets offer the possibility of greater markets sales, whereas expanding markets are frequently characterized by competitive instability that may favor new products Jacobson & Aaker 1987; Phillips, Chang, & Buzzell 1983; Song, Souder, & Dyer 1997 - Many competitors Yoon & Lilien 1985 - Low product quality New industrial products have been shown to enjoy a higher success rate in markets with a small number of competitors 31 Brown & Eisenhardt 1995; Cooper & Kleinschmidt 1987; Zirger & Maidique 1985 - Hostile markets Environmental hostility moderates the impact of NPD proficiency on success Calantone, Schmidt & Di Benedetto, 1997 Adverse market conditions (cont’d) - Markets with Frequent new product introductions drive demand Redmond 1995 frequent new fragmentation and oversaturation of distribution product Industries characterized by a historically high rate introductions of new product introductions, such as consumer packaged goods, offer inherently less fertile ground for new products The list of references cannot claim to be comprehensive Where possible, it includes review papers where the reader can find additional references Henard and Szymanski’s meta-analysis shows the factor to be marginally not significant However, other important studies rank it as highly important Hypothesis lacks empirical evidence in the context of new product performance 32 cast* fidence Table Descriptive Statistics and Correlations for Key Constructs in the Full Sample (n=444) and Subsets of New Product Launches by Overconfident (n=81) and Nonoverconfident (n=363) Decision Makers Full Sample Mean 24 SD 37 Overconf.=0 Mean 22 SD Overconf.=1 Mean 35 32 SD 41 18 39 00 00 1.00 00 10 4.91 2.22 4.93 2.20 4.81 2.30 -.04 -.02 19.91 7.76 19.50 7.30 21.73 9.38 49 11 -.02 8.43 5.70 8.41 5.84 8.57 5.07 -.07 01 04 -.11 pend 8.64 3.92 8.77 3.97 8.08 3.68 -.12 -.07 02 -.23 21 rice 18.41 2.19 18.35 2.18 18.68 2.23 07 06 -.12 29 -.21 -.33 ucts 21.51 2.48 21.58 2.48 21.20 2.47 -.06 -.06 01 -.23 50 46 -.31 411.34 107.47 407.81 106.60 430.60 110.03 -.03 08 -.02 -.05 29 29 -.02 -.08 2.15 4.31 2.19 4.70 2.00 1.73 04 -.02 -.03 04 02 01 01 -.06 -.04 60 49 61 49 56 50 00 -.05 -.05 00 05 -.10 08 01 12 4.88 79 4.81 79 5.22 71 02 20 00 07 -.02 00 03 * Correlations with OverforecastOrd (not shown for parsimony reasons) are identical in magnitude and significance to those for Overforecast Firm and industry-average expenditure data, as well as firm market valuations, are in millions Correlations with absolute values greater than 08, 09 and 12 are significant at p < 1, p < 05 and p < 01, respectively -.02 -.02 Spend aluation p m 33   Table Results for Cumulative Logit (Models 1-3), OLS (Models 4-5) Regressions, and Sobel Test Model OverforecastOrd d ion Coeff -.226 -1.151*** 676 060 001 026 090 140 ce R 444 044 16.09** SE 206 399 955 053 001 023 220 137 Model OverforecastOrd Coeff -.254 -1.100*** 637 060 001 027 117 086 490* 444 054 19.41** 010 SE 205 400 959 053 001 023 221 140 265 Model OverforecastOrd Coeff -.296 -1.006** -1.704 108* 001 019 011 050 330 -.046 143*** 444 11 262 101.6*** SE 227 432 1.099 057 001 025 239 154 297 054 018 204 tistic Model Overforecast Coeff -0.045 -0.160*** 0.072 0.007 0.005 0.015 0.014 SE 0.033 0.060 0.156 0.009 0.004 0.036 0.022 Model Overforecast Coeff -.048 -.149** 066 007 005 017 006 090** 444 444 031 1.75* 041 2.03** 010 3.89** 2.24** * p < 1; ** p < 05; *** p < 01 The models included an intercept (cumulative logit fits an intercept for each variable class) that are not shown 34 SE 032 060 156 009 004 036 022 046 M Ove Coe -.048 -.104 -.290 01 00 -.00 -.00 04 -.00 024* 44 25 13.58* 21 50.13* 2.30 pendent variable: mMktSpend MktSpend AvgPrice Products AvgValuation rkExp EM imism erconfidence S e DSS C Chi-sq x-rescaled R atistic Table Results of Additional Analyses Model OverforecastOrd Coeff SE -.294 227 -1.018** 431 -1.678 1.101 108* 057 001 001 019 025 015 239 052 154 328 297 062 143*** 018 -.037 444 11 101.27*** 261 Model Price Coeff SE 1.596** 690 654*** 444 033 Model AFE Coeff .244** 366* -.920* 003 000 012 -.003 -.092 139 SE 105 193 504 028 001 013 116 072 149 Model 10 Demand Coeff 263.567*** 280.592*** 60.695 -38.567*** 027 -5.738 2.943 -17.683 39.101 21.307 -47.127*** 444 11 478 059 342 203.40*** 2.99*** 20.22*** * p < 1; ** p < 05; *** p < 01 The models included an intercept (cumulative logit fits an intercept for each variable class) that are not shown 35 54.0 100.0 266.5 14.2 6.7 60.0 37.3 77.5 13.1 3.9 Environment Faulty Market Research Incentive Incompatibility Analysis  and   Interpreta0on   Mis-forecasts Decision Unit’s Limitations Decision   Response   High-risk or Erroneous Project Selection and Continuation Decisions Weak Value Proposition Resource Limitations Organizational Impediments Execu0on   Flawed Marketing Programs Low Product Quality The Firm Figure A General Model of Antecedents of New Product Failure in the Marketplace 36 Com Ma APPENDIX A Items Used to Measure Key Constructs Overconfidence: This simulation exercise involves managing a virtual firm in competition against firms managed by other participants To this end, you will be required to make a full spectrum of business decisions pertaining to operations, marketing and finance Given your current level of preparedness, how you expect to perform relative to the other participants beginning the exercise with you today? Bottom 20% Lower 21-40% Middle 41-60% Upper 61-80% Top 20% Dispositional Optimism based on the Revised Life Orientation Test scale Please indicate your agreement or disagreement with the following statements (The 7-point scale is anchored by “Strongly disagree” = 1, “Neither agree nor disagree” = 4, and “Strongly agree” = a In uncertain times, I usually expect the best b If something can go wrong for me, it will (reverse coded) c I am always optimistic about my future d I hardly ever expect things to go my way (reverse coded) e I rarely count on good things happening to me (reverse coded) f Overall, I expect good things to happen to me rather than bad Decision Support System (DSS) reports and tools* a Market share report (shows market share by product by market segment for all products) b Customer analysis report (shows segment characteristics, preferences and projected evolution) c Competitor analysis report (shows competitors’ pricing, market awareness about each product, and customer perceptions of all products on key attributes) d Advertising performance report (shows the effectiveness of the firm’s advertising campaigns relative to actual performance and perceptual objectives) e Income statement f Cash flow statement g Investment report (shows the net present value of firm investments) h Valuation report (shows firm market valuation over time) i Product attributes calculator (a dynamic tool that allows participants to estimate product performance attributes given various levels product characteristics)** j Profit-and-loss calculator (a dynamic tool that allows participants to estimate profit or loss given the firm’s sales projections and current cost structure)** * The SIG provides automatic display of the Industry performance report, Production report and Balance sheet To the extent that all participants see these baseline reports, we not include them in the DSS variable ** Our treatment of the two dynamic tools is categorical (used/not used), similar to how we treat the other reports We separately consider the number of estimates run in each calculator in our robustness checks 37